Introduction: promoting restraint in war
Bibliographic record
Abstract
Over the last decade, changes in the nature of conflict have generated profound operational challenges for international humanitarian organizations. The number of non-international armed conflicts doubled between 2001 and 2016, rising from fewer than 30 to more than 70. The number of armed groups fighting in them has also multiplied: more armed groups emerged in the last decade than in the previous century. Humanitarian organizations struggle to assist victims of these armed conflicts and to persuade fighters to act with restraint toward those individuals who are not, or no longer, taking part in hostilities. New research was required to identify sources of influence on battlefield restraint to inform operational activities. We present a theoretical framework that identifies the sources of norms of restraint in state and non-state armed groups. We argue that humanitarian organizations ought to broaden their notions of the processes that influence the socialization and uptake of norms of restraint and mobilize new societal actors to the cause of limiting violence. In our framing of the empirical articles in the collection, we argue that the structure of armed organizations and their embeddedness in local communities heavily influence how group norms and internal rules are formed and reinforced. While hierarchical militaries can largely be influenced by top-down discipline, restraint among more decentralized armed groups is strongly influenced by societal actors external to the group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".